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Emotion detection and Sentiment analysis techniques are used to understand polarity or emotions expressed by people in many cases, especially during interactive systems use. Recognizing users' emotions is an important topic for human–computer interaction. Computers that recognize emotions would provide more natural interactions. Also, emotion detection helps design human-centred systems that provide adaptable behaviour change interventions based on users' emotions. The growing capability of machine learning to analyze big data and extract emotions therein has led to a surge in research in this domain. With this increased attention, it becomes essential to investigate this research area and provide a comprehensive review of the current state. In this paper, we conduct a systematic review of 123 papers on machine learning-based emotion detection to investigate research trends along many themes, including machine learning approaches, application domain, data, evaluation, and outcome. The results demonstrate: 1) increasing interest in this domain, 2) supervised machine learning (namely, SVM and Naïve Bayes) are the most popular algorithms, 3) Text datasets in the English language are the most common data source, and 4) most research use Accuracy to evaluate performance. Based on the findings, we suggest future directions and recommendations for developing human-centred systems. [ABSTRACT FROM AUTHOR]

A significant proportion of smartphone notifications are indicative of human behaviour (e.g. delivery updates for purchased items, physical activity summaries, and notification of updates to subscribed content). However, present attempts to understand human behaviour from smartphone traces typically focus on sensors such as location, accelerometer and proximity, overlooking the potential for notifications as a valuable data source. In this paper, we propose a general framework that provides end-to-end processing of notifications to understand behavioural aspects. We realise the framework with an implementation that tackles the specific use case of establishing prior buying behaviour from associated notifications. To evaluate the framework and implementation, we conduct a longitudinal user study in which we collect more than 250, 000 notifications, from twelve users, over an average of three months. We apply knowledge-based and machine learning techniques to those notifications to assess the tasks of the proposed framework. The results show a substantial difference in the performance between the methods used to extract behavioural features from the collected notifications. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of users' personality traits and emotions expressed through textual communications on YouTube to detect cyberbullying using a series of ensemble classifiers. Personality traits were determined using the Big Five model whereas emotions were based on Ekman's basic emotion theory. Annotated YouTube textual comments in English (N = 5152; i.e. 2576 number of bullying versus 2576 non-bullying instances) were used to detect cyberbullying incidents using several ensemble classifiers, including Random Forest and AdaBoost. Performance metrics revealed both personality traits and emotion to significantly improve the identification of cyberbullying presence, with accuracy and F-score values of more than 95%. Further fine-grained analysis revealed anger and openness to be more profound compared to other emotions and personalities. Further, neurotic individuals tend to be driven to cyberbullying by joy, disgust and fear. The findings show that personality and emotions play pertinent roles in cyberbullying, and the identification of specific traits and emotions can help in designing a more strategic intervention programme. [ABSTRACT FROM AUTHOR]

This paper examines creators' contributions and the incentive effects of users' feedback, including upvoting, sharing, commenting, following, viewing comments, and clicking on the creators' homepage. We build a negative binomial and ordinary least squares (OLS) regression model using data from a Chinese music streaming company NetEase Cloud Music. Results show that creators' number of content contributions is positively affected by the number of users' commenting, viewing comments, sharing, and clicking on homepage behaviors. On the other hand, users' upvotes and sharing behaviors can significantly influence the quality of content published by creators. Moreover, we find that the intensity level of creators has moderate effects on such influence. These findings help the researchers better understand creators' behaviors and community managers to build user-generated content (UGC) communities. [ABSTRACT FROM AUTHOR]

Excessive use of personal social media has become prevalent in the work environment and has caused significant psychological and behavioural problems among employees. These problems call for in-depth study. In this paper, we develop a usage experience–stressor–strain model to examine the consequences of excessive social media usage behaviour of employees at work, revealing the effects of different excessive usage patterns (i.e. social, hedonic, and cognitive) on employees' psychological state (i.e. emotional exhaustion) and behavioural outcome (i.e. job performance) through dimensions of social media overload (i.e. information, communication, and social). We empirically test the research model with a survey of 422 employees who use social media at work. Our analysis presents that excessive social use at work is the major predictor of dimensions of overload. Furthermore, in contrast to the single effect of communication overload on emotional exhaustion and social overload on job performance, information overload has a significant impact on both emotional exhaustion and job performance of employees. This study advances our understanding of the complex relationships between excessive usage patterns, overload, emotional exhaustion, and job performance. [ABSTRACT FROM AUTHOR]

This paper presents key aspects that designers and Human–Computer Interaction practitioners might encounter when designing multimodal interaction for older adults, focusing on the trade-offs that might occur as part of the design process. The paper gathers literature on multimodal interaction and assistive technology, and describes a set of design challenges specific for older users. Building on these main design challenges, four trade-offs in the design of multimodal technology for this target group are presented and discussed. To highlight the relevance of the trade-offs in the design process of multimodal technology for older adults, two of the four reported trade-offs are illustrated with two user studies that investigate mid-air and speech-based interaction with a tablet device. The first study explores the design trade-offs related to redundant multimodal commands in older, middle-aged and younger adults, whereas the second one investigates the design choices related to the definition of a set of mid-air one-hand gestures and voice input commands for older adults. Further reflections highlight the design trade-offs that such considerations bring in the process, providing an overview of the design choices involved and of their potential consequences. [ABSTRACT FROM AUTHOR]

There is a growing tendency for users to expect conversational agents (CAs) to recognise social cues and follow interpersonal communication principles to enhance their subjective evaluation. Therefore, this paper studies how personal pronouns should be used by CAs in response to users. We conducted a 3 (CAs' personal pronoun) × 3 (users' personal pronoun) × 2 (participants'gender) mixed design. this study used mixed methods based on an experimental design, including ratings, forced choices and interviews, for mutual confirmation. The findings indicate that first, users prefer that CAs use second-person pronouns. Second, there is also turn-taking and convergence tendency between users and CAs in personal pronoun use. Third, there are gender differences in personal pronoun preferences and relationship positions toward CAs. These results can inform personalised voice interaction and humanlike design and help build closer relationships between users and CAs in future human–computer interactions. [ABSTRACT FROM AUTHOR]

Social networking sites and apps (SNSAs) are being used more frequently across the world and yet the nature of this online environment and associated interactions are not fully understood. With no restrictions for SNSAs use related to specific geographical regions, language, age, gender, educational level, or any other factors, it is important to find a concept to describe and explain the components of this online environment and their relationships. This paper describes the environment of SNSAs use by Saudi postgraduate students from the perspective of second-generation activity theory (AT). The findings supported the theoretical framework of AT as a useful lens in understanding SNSAs use from the perspective of students, particularly in a cultural environment where physical communication has restrictions based on gender. A similar AT model can be drawn for the use of SNSAs, taking the perspective of different groups of academic users. The flexibility of the AT model appears at the level of SNSAs as technical and physical tools, and at the level of users' community, which was managed by the explicit and implicit rules relating to communication. This feature reveals the way in which this extended framework can be used to indicate pertinent features of SNSAs. [ABSTRACT FROM AUTHOR]

Previous research has established that leaders in information and communication technology (ICT) are crucial for establishing a user-centred systems design perspective in ICT for work-related tasks. This paper therefore describes the perspectives of 18 ICT leaders in three kinds of leadership roles (managers, project leaders and specialists) in order to understand their views of user-centred systems design concerning ICT. It uses the concept of technological frames of reference to analyse three domains: technology-in-use, technology strategy and nature of technology. The results show that many specialists see user involvement as a critical factor in successfully establishing new information and communication technologies, but that these systems are currently built around the needs of management rather than end users. Looking forward, all three relevant social groups are optimistic about how ICT will become more user-centred and more strategically aligned in the future. However, changes in ICT are described as extremely energy-consuming and difficult – akin to 'walking in the jungle with a machete'. Finally, we discuss the relevance of technological frames and present some implications for the successful establishment of user-centred system design as a perspective in organisations. [ABSTRACT FROM AUTHOR]

For organisations utilising big data platforms, knowledge sharing helps spread contextualised information of data, increases efficiencies and reduces the cost of lost knowledge when employees leave. Query-Driven Knowledge Sharing Systems (QKSS) partially automate knowledge sharing in analytics teams by building context into data, enabling the reuse of complex queries. Although QKSS can improve knowledge sharing, encouraging reuse behaviour is a significant issue for adoption. This paper analyses the applicability of gamification for improving knowledge reuse in QKSS. In collaboration with a Sydney-based data analytics firm, we recruited professional data analysts to participate in an experiment. The recruited analysts were asked to complete Structured Query Language tasks using either the firm's QKSS platform or a gamified version which included a small number of gamified elements designed to increase the likelihood of query reuse. The results demonstrate the positive impact of gamification on query reuse and the efficiency of tasks. [ABSTRACT FROM AUTHOR]

A major issue in the digital age is how to safeguard the security of the massive amount of data being stored, processed and transferred through digital channels. Organisational communication research into data security shows that people are the weakest link. Non-compliant behaviour can lead to security breaches. Existing studies have focused above all on how employees can be motivated to comply with data security procedures. However, focusing on desirable behaviour does not explain why people often ignore security regulations. In addition, existing research tends to focus primarily on self-reported attitudes and perceptions, which can give a distorted impression of people's actual behaviour. In this paper, we address these issues by combining individual, habitual and situational factors to explain non-compliant behaviour in a vignette study, using SmartPLS to analyse survey data from 651 subjects in a large Dutch government organisation. The results indicate that bad habits play a significant role in non-compliant behaviour. This behaviour is fuelled by situational factors like time pressure, while a lack of self-efficacy also increases non-compliant behaviour. Based on these results, a communication strategy that addresses bad habits in a situational context may provide an alternative way to improve people's compliant behaviour. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the underlying mechanism of the community climate on knowledge-sharing intention in virtual communities. Based on social cognitive theory, a theoretical model was developed and empirically tested using a web-based survey of 525 members from an online question and answer (Q&A) community in China. Results showed that knowledge-sharing self-efficacy and outcome expectation played a chain mediation role in the relationship between the community climate and knowledge-sharing intention. This study is among the first to examine whether and how community climate influences knowledge sharing. Our research findings unpack the mechanism of the community climate on knowledge-sharing intention, and provide practical insights on how to use community climate to facilitate users to share knowledge in online Q&A communities. [ABSTRACT FROM AUTHOR]